Kenneth T. Co, PhD
Assistant Professor
Academic Program Director
Master in Data Analytics
EXPERTISE
Machine Learning, Artificial Intelligence, Cybersecurity, Computer Vision, Data Science
RESEARCH INTERESTS
Data-centric Machine Learning, AI in Healthcare, Anomaly Detection, Adversarial Machine Learning, Safety of Artificial Intelligence
Kenneth T. Co, PhD
Assistant Professor
Academic Program Director, Master in Data Analytics
ACADEMIC BACKGROUND
- PhD in Computing, Imperial College London
- Master of Science in Computing (Machine Learning), Imperial College London
- Master of Arts in Mathematics, Johns Hopkins University
- Bachelor of Arts in Mathematics, Johns Hopkins University
PROFESSIONAL AND ACADEMIC EXPERIENCE
- Machine Learning Consultant, Thinking Machines
- Research Scientist, DataSpartan UK
AFFILIATIONS, AWARDS, AND HONORS
- Endorsement for Global Talent Visa, Royal Academy of Engineering, United Kingdom
- 3rd Place, CSAW’20 Cybersecurity Applied Research Competition, Europe, hosted by New York University (NYU) and Grenoble INP-ESISAR
- PhD Programme Funding, Department of Computing, Imperial College London
- J.J. Sylvester Undergraduate Award, Department of Mathematics, Johns Hopkins University
- Applied Mathematics and Statistics Achievement Award, Department of Applied Mathematics and Statistics, Johns Hopkins University
- Naddor Prize, Department of Applied Mathematics and Statistics, Johns Hopkins University
- Woodrow Wilson Research Fellow, Johns Hopkins University
- Bronze Medalist, International Mathematical Olympiad (IMO), Mar del Plata, Argentina
PEER-REVIEWED JOURNAL
- Sunmola, F., Baryannis, G., Tan, A., Co, K., & Papadakis, E. (2025). Holistic Framework for Blockchain-Based Halal Compliance in Supply Chains Enabled by Artificial Intelligence. Systems, 13(1), 21. https://doi.org/10.3390/systems13010021
PEER-REVIEWED CONFERENCE PROCEEDINGS
- Castiglione, L. M., Hau, Z., Co, K. T., Muñoz-González, L., Teng, F., & Lupu, E. (2022). HA-Grid: Security Aware Hazard Analysis for Smart Grids. In Proceedings of the 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 446-452. https://doi.org/10.1109/SmartGridComm52983.2022.9961003
- Co, K. T., Muñoz-González, L., Kanthan, L., Glocker, B., & Lupu, E. C. (2021). Universal adversarial robustness of texture and shape-biased models. In Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), 799-803. https://doi.org/10.1109/ICIP42928.2021.9506325
- Co, K. T., Muñoz-González, L., de Maupeou, S., & Lupu, E. C. (2019). Procedural noise adversarial examples for black-box attacks on deep convolutional networks. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS ’19), 275–289. https://doi.org/10.1145/3319535.3345660
BOOK CHAPTER
- Muñoz-González, L., Carnerero-Cano, J., Co, K. T., & Lupu, E. C. (2019). Challenges and Advances in Adversarial Machine Learning. Resilience and Hybrid Threats, 102-120.
